COVID19

Libraries

library(httr)
library(lubridate)
library(ggplot2)
library(ggrepel)
library(patchwork)
library(data.table)
library(broom)
library(rgdal)


options(scipen=2)

Load and process map and current data

# Download the shape file from the web and unzip it:
# download.file("http://thematicmapping.org/downloads/TM_WORLD_BORDERS_SIMPL-0.3.zip", destfile="~/world_shape_file/world_shape_file.zip")
# system("unzip ~/world_shape_file/world_shape_file.zip")
world_spdf <- readOGR(dsn='~/world_shape_file/', layer='TM_WORLD_BORDERS_SIMPL-0.3')

## OGR data source with driver: ESRI Shapefile 
## Source: "/Users/kimon.froussios/world_shape_file", layer: "TM_WORLD_BORDERS_SIMPL-0.3"
## with 246 features
## It has 11 fields
## Integer64 fields read as strings:  POP2005

tf <- "~/covid19.csv"
GET("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", authenticate(":", ":", type="ntlm"), write_disk(tf, overwrite = TRUE))

## Response [https://opendata.ecdc.europa.eu/covid19/casedistribution/csv/]
##   Date: 2020-09-30 17:01
##   Status: 200
##   Content-Type: application/octet-stream
##   Size: 3.16 MB
## <ON DISK>  /Users/kimon.froussios/covid19.csv

DT <- fread(tf)
setnames(DT, c('dateRep', 'day', 'month', 'year', 'new_Cases', 'new_Deaths', 'Country', 'geoId', 'countryCode', 'population', 'continent', 'roll_norm_Cases'))
DT[, Date := dmy(dateRep)]

# Dataframe-ize.
world_df <- as.data.table(tidy(world_spdf, region="ISO3"))

## SpP is invalid

# For plottting, the order of rows is super important. 
# Merge operations later can change the order, so I need to be able to recover it.
world_df[, ord := 1:nrow(world_df)]

# Sort newest last.
setorder(DT, Country, year, month, day)

# Total Cases and Deaths
DT[, total_Cases := cumsum(new_Cases), by=Country]
DT[, total_Deaths := cumsum(new_Deaths), by=Country]
DT[, Mortality := total_Deaths / total_Cases, by=Country]

# Sliding window cumulative cases in a W-days window.
W <- 14
DT[, roll_Cases := frollsum(new_Cases, n=W, align='right'), by=Country]
DT[, roll_Deaths := frollsum(new_Deaths, n=W, align='right'), by=Country]

# Remove all lines with no info
#DT <- DT[total_Cases > 0,]


# Day relative to first reported case
DT[, Day := 1:length(.SD$new_Cases), by=Country]

# Day relative to N deaths
N <- 100
DT[, Nplus := abs(total_Deaths - N)]
DT[, Day_Aligned := Day - .SD[Nplus==min(Nplus), Day][1], by=Country]
DT[, Nplus := NULL]

# Day relative to today
DT[, past_Days := Day - max(Day), by=Country]

# Population normalisations to P citizens
P <- 1e6
DT[, norm_new_Cases := new_Cases / population * P, by=Country]
DT[, norm_new_Deaths := new_Deaths / population * P, by=Country]
DT[, norm_tot_Cases := total_Cases / population * P, by=Country]
DT[, norm_tot_Deaths := total_Deaths / population * P, by=Country]
DT[, norm_roll_Cases := roll_Cases / population * P, by=Country]
DT[, norm_roll_Deaths := roll_Deaths / population * P, by=Country]

# Global
DT[, gNew_Cases := sum(new_Cases, na.rm=TRUE), by=Date]
DT[, gNew_Deaths := sum(new_Deaths, na.rm=TRUE), by=Date]
DT[, gTotal_Cases := sum(total_Cases, na.rm=TRUE), by=Date]
DT[, gTotal_Deaths := sum(total_Deaths, na.rm=TRUE), by=Date]
DT[, gRoll_Cases := sum(roll_Cases, na.rm=TRUE), by=Date]
DT[, gRoll_Deaths := sum(roll_Deaths, na.rm=TRUE), by=Date]
DT[, gMortality := gTotal_Deaths / gTotal_Cases]

# Rates of daily change
DT[, roll_Cases_Rate := frollapply(roll_Cases, n=2, FUN = function(x){x[2] / x[1]}, align="right"), by=Country]
DT[, roll_Deaths_Rate := frollapply(roll_Deaths, n=2, FUN = function(x){x[2] / x[1]}, align="right"), by=Country]
DT[, gRoll_Cases_Rate := frollapply(gRoll_Cases, n=2, FUN = function(x){x[2] / x[1]}, align="right"), by=Country]
DT[, gRoll_Deaths_Rate := frollapply(gRoll_Deaths, n=2, FUN = function(x){x[2] / x[1]}, align="right"), by=Country]

# Compare severity to one country.
homecountry = 'Austria'

The Covid10 dataset from ECDC comes without geospacial data. The geospacial data available from other sources may not be the most up-to-date with recognised countries and names.

message(paste(sum(!( world_spdf$ISO2 %in% DT$geoId | world_spdf$ISO3 %in% DT$countryCode)), 
                            "countries in the map file do not correspond to an entry in the Covid19 data."))

## 42 countries in the map file do not correspond to an entry in the Covid19 data.

outgroup <- unique(DT[!(geoId %in% world_spdf$ISO2 | countryCode %in% world_spdf$ISO3), 
                                            .(Country, countryCode, geoId)])
message(paste(nrow(outgroup),
                            "countries in the Covid19 data do not correspond to an entity in the map file:"))

## 6 countries in the Covid19 data do not correspond to an entity in the map file:

print(outgroup)

##                                       Country countryCode    geoId
## 1:          Bonaire, Saint Eustatius and Saba         BES       BQ
## 2: Cases_on_an_international_conveyance_Japan             JPG11668
## 3:                                    Curaçao         CUW       CW
## 4:                                     Kosovo         XKX       XK
## 5:                               Sint_Maarten         SXM       SX
## 6:                                South_Sudan         SSD       SS

One of those entries corresponds to a ship, leaving only 5 Countries not represented in the map file. I think for a global overview map, the missing countries will not make a big difference.

Status

minpop=5e5
current <- max(DT$Date)

print( data.frame(DaysTracked = length(unique(DT$Date)),
                    CountriesTracked = length(unique(DT$Country)) ) )

##   DaysTracked CountriesTracked
## 1         275              210

print( data.frame( Global_Cases = sum(DT$new_Cases),
                    Global_Deaths = sum(DT$new_Deaths),
                    Global_Mortality = sum(DT$new_Deaths) / sum(DT$new_Cases) ) )

##   Global_Cases Global_Deaths Global_Mortality
## 1     33714595       1008932       0.02992567

Reported events since the beginning

Normalized per 10^{6} residents.

subDT1 <- merge(world_df, DT[past_Days==0, .(countryCode, continent, norm_tot_Cases)], by.x='id', by.y='countryCode')
setorder(subDT1, ord)

subDT2 <- merge(world_df, DT[past_Days==0, .(countryCode, continent, norm_tot_Deaths)], by.x='id', by.y='countryCode')
setorder(subDT2, ord)

p1 <-   ggplot(subDT1, aes(x=long, y=lat, group=group, fill=norm_tot_Cases)) +
        geom_polygon(colour='black', size=0.2) +
        scale_fill_gradient(high='darkred', low='white') +
        theme_void() +
        theme(panel.background = element_rect(fill='#BBDDFF'))

p2 <-   ggplot(subDT2, aes(x=long, y=lat, group=group, fill=norm_tot_Deaths)) +
        geom_polygon(colour='black', size=0.2) +
        scale_fill_gradient(high='darkgreen', low='white') +
        theme_void() +
        theme(panel.background = element_rect(fill='#BBDDFF'))

print( p1 )

print( p2 )

p1 <-   ggplot(subDT1[continent=='Europe',], aes(x=long, y=lat, group=group, fill=norm_tot_Cases)) +
        geom_polygon(colour='black', size=0.2) +
        scale_fill_gradient(high='darkred', low='white') +
      coord_cartesian(xlim=c(-10, 50), ylim=c(30,70)) +
        theme_void() +
        theme(panel.background = element_rect(fill='#BBDDFF'))

p2 <-   ggplot(subDT2[continent=='Europe',], aes(x=long, y=lat, group=group, fill=norm_tot_Deaths)) +
        geom_polygon(colour='black', size=0.2) +
        scale_fill_gradient(high='darkgreen', low='white') +
      coord_cartesian(xlim=c(-10, 50), ylim=c(30,70)) +
        theme_void() +
        theme(panel.background = element_rect(fill='#BBDDFF'))


print( p1 )

print( p2 )

Reported events in last 14 days

Normalized per 10^{6} residents.

subDT1 <- merge(world_df, DT[past_Days==0, .(countryCode, continent, norm_roll_Cases)], by.x='id', by.y='countryCode')
setorder(subDT1, ord)

subDT2 <- merge(world_df, DT[past_Days==0, .(countryCode, continent, norm_roll_Deaths)], by.x='id', by.y='countryCode')
setorder(subDT2, ord)

p1 <-   ggplot(subDT1, aes(x=long, y=lat, group=group, fill=norm_roll_Cases)) +
        geom_polygon(colour='black', size=0.2) +
        scale_fill_gradient(high='darkred', low='white') +
        theme_void() +
        theme(panel.background = element_rect(fill='#BBDDFF'))

p2 <-   ggplot(subDT2, aes(x=long, y=lat, group=group, fill=norm_roll_Deaths)) +
        geom_polygon(colour='black', size=0.2) +
        scale_fill_gradient(high='darkgreen', low='white') +
        theme_void() +
        theme(panel.background = element_rect(fill='#BBDDFF'))

print( p1 )

print( p2 )

p1 <-   ggplot(subDT1[continent=='Europe',], aes(x=long, y=lat, group=group, fill=norm_roll_Cases)) +
        geom_polygon(colour='black', size=0.2) +
        scale_fill_gradient(high='darkred', low='white') +
      coord_cartesian(xlim=c(-10, 50), ylim=c(30,70)) +
        theme_void() +
        theme(panel.background = element_rect(fill='#BBDDFF'))

p2 <-   ggplot(subDT2[continent=='Europe',], aes(x=long, y=lat, group=group, fill=norm_roll_Deaths)) +
        geom_polygon(colour='black', size=0.2) +
        scale_fill_gradient(high='darkgreen', low='white') +
      coord_cartesian(xlim=c(-10, 50), ylim=c(30,70)) +
        theme_void() +
        theme(panel.background = element_rect(fill='#BBDDFF'))

print( p1 )

print( p2 )

Rate of change since the day before.

This looks at how the 14-day rolling totals changed from yesterday to today. This is more stable than looking only at the new cases.

subDT1 <- merge(world_df, DT[past_Days==0, .(countryCode, continent, roll_Cases_Rate)], by.x='id', by.y='countryCode')
setorder(subDT1, ord)

subDT2 <- merge(world_df, DT[past_Days==0, .(countryCode, continent, roll_Deaths_Rate)], by.x='id', by.y='countryCode')
setorder(subDT2, ord)

p1 <-   ggplot(subDT1, aes(x=long, y=lat, group=group, fill=roll_Cases_Rate)) +
        geom_polygon(colour='black', size=0.2) +
        scale_fill_gradient(high='darkred', low='white') +
        theme_void() +
        theme(panel.background = element_rect(fill='#BBDDFF'))

p2 <-   ggplot(subDT2, aes(x=long, y=lat, group=group, fill=roll_Deaths_Rate)) +
        geom_polygon(colour='black', size=0.2) +
        scale_fill_gradient(high='darkgreen', low='white') +
        theme_void() +
        theme(panel.background = element_rect(fill='#BBDDFF'))

print( p1 )

print( p2 )

p1 <-   ggplot(subDT1[continent=='Europe',], aes(x=long, y=lat, group=group, fill=roll_Cases_Rate)) +
        geom_polygon(colour='black', size=0.2) +
        scale_fill_gradient(high='darkred', low='white') +
      coord_cartesian(xlim=c(-10, 50), ylim=c(30,70)) +
        theme_void() +
        theme(panel.background = element_rect(fill='#BBDDFF'))

p2 <-   ggplot(subDT2[continent=='Europe',], aes(x=long, y=lat, group=group, fill=roll_Deaths_Rate)) +
        geom_polygon(colour='black', size=0.2) +
        scale_fill_gradient(high='darkgreen', low='white') +
      coord_cartesian(xlim=c(-10, 50), ylim=c(30,70)) +
        theme_void() +
        theme(panel.background = element_rect(fill='#BBDDFF'))

print( p1 )

print( p2 )

Countries of personal interest

topInterest <- c('Austria', 'Italy', 'Greece', 'Luxembourg')
setorder(DT, Country, past_Days)

Normalizations to 10^{6} residents.

In last 14 days

DT[past_Days==0 & Country %in% topInterest, .(Country, norm_roll_Cases, roll_Cases, norm_roll_Deaths, roll_Deaths)]

##       Country norm_roll_Cases roll_Cases norm_roll_Deaths roll_Deaths
## 1:    Austria       1113.3594       9863         4.402415          39
## 2:     Greece        409.6190       4393         6.993268          75
## 3:      Italy        381.3978      23021         4.009308         242
## 4: Luxembourg       1868.4007       1147         0.000000           0

Since the beginning

DT[past_Days==0 & Country %in% topInterest, .(Country, norm_tot_Cases, total_Cases, norm_tot_Deaths, total_Deaths)]

##       Country norm_tot_Cases total_Cases norm_tot_Deaths total_Deaths
## 1:    Austria       5035.346       44607        89.85441          796
## 2:     Greece       1689.853       18123        36.17851          388
## 3:      Italy       5185.775      313011       594.35503        35875
## 4: Luxembourg      13733.641        8431       201.98927          124

Timeline

case_col = '#FF0000'
death_col = '#0088FF'

relative_plot <- function(df, sel_country, value_colx, value_coly, colour) {
    ggplot(NULL, aes_string(x=value_colx, y=value_coly, group='Country')) +
        geom_line(data=df, alpha=0.1, size=0.25) +
        geom_line(data=df[Country==sel_country,], colour=colour, size=0.75) +
        scale_x_log10() +
        scale_y_log10() +
        coord_cartesian(xlim=c(100,NA), ylim=c(1,NA)) +
        annotation_logticks(sides='lrbt') +
        # labs(title=title) +
        theme_bw()
}

rate_plot <- function(df, sel_country, value_col, title, colour) {
    ggplot(NULL, aes_string(x='Date', y=value_col, group='Country')) +
        geom_hline(yintercept = 1, size=0.25) +
      # geom_line(data=df, alpha=0.1, size=0.15) +
        geom_line(data=df[Country==sel_country,], colour=colour, size=0.5) +
        coord_cartesian(ylim=c(0.8, 1.2)) +
        labs(title=title, x='') +
        theme_bw()
}

Global

subDT <- melt(unique(DT[, .(Date, gNew_Cases, gNew_Deaths, gTotal_Cases, gTotal_Deaths, gRoll_Cases, gRoll_Deaths)]),
                            id.vars="Date", variable.name="Type", value.name="Events")

p1 <- ggplot(subDT, aes(x=Date, y=Events, colour=Type, fill=Type)) +
    facet_grid( sub('^g', '', sub('_Cases', '', sub('_Deaths', '', subDT$Type))) ~ ., scales = 'free_y') +
    geom_line() +
    theme_minimal() + 
    labs(x='', y='') +
    theme(legend.position='none')

p2 <- ggplot(subDT, aes(x=Date, y=Events, colour=Type, fill=Type)) +
    facet_grid( sub('^g', '', sub('_Cases', '', sub('_Deaths', '', subDT$Type))) ~ ., scales = 'free_y') +
    geom_line() +
    scale_y_log10() +
    labs(x='', y='') +
    theme_minimal()

print( p1 + p2 )

subDT <- unique(DT[, .(Date, gRoll_Cases_Rate, gRoll_Deaths_Rate, gMortality)])

p3 <- ggplot(subDT, aes(x=Date, y=gRoll_Cases_Rate)) +
    geom_hline(yintercept = 1, size=0.1) +
    geom_line(colour=case_col, size=0.5) +
    coord_cartesian(ylim=c(0.9, 1.1)) +
    labs(title=paste('Daily change rate of ', W, '-day rolling sum')) +
    theme_bw()

p4 <- ggplot(subDT, aes(x=Date, y=gRoll_Deaths_Rate)) +
    geom_hline(yintercept = 1, size=0.1) +
    geom_line(colour=death_col, size=0.5) +
    coord_cartesian(ylim=c(0.9, 1.1)) +
    labs(title='') +
        theme_bw()

p5 <- ggplot(subDT, aes(x=Date, y=gMortality)) +
    geom_hline(yintercept = 0, size=0.1) +
    geom_line(colour=death_col, size=0.5) +
    labs(title='') +
        theme_bw()

print( p3 / p4 / p5)

Austria

i <- 'Austria'
message(i)

## Austria

subDT <- melt(DT[, .(Date, Country, norm_new_Cases, norm_new_Deaths, norm_tot_Cases, norm_tot_Deaths, norm_roll_Cases, norm_roll_Deaths)],
                            id.vars = c('Date', 'Country'), variable.name = 'Type', value.name = 'Normalized_count')
subDT[grepl('Death', Type), vsplit := 'Deaths']
subDT[!grepl('Death', Type), vsplit := 'Cases']
subDT[, hsplit := sub('_Cases|_Deaths', '', sub('norm_', '', Type), perl=TRUE)]
    
p1 <- ggplot(subDT, aes(x=Date, y=Normalized_count, group=Country, colour=vsplit)) +
    facet_grid(hsplit ~ vsplit, scales = 'free_y') +
    geom_line(colour='#000000', alpha=0.1, size=0.25) +
    geom_line(data=subDT[Country==i,], size=0.75) +
    scale_y_log10() +
    scale_colour_manual(values=c(Deaths=death_col, Cases=case_col)) +
    coord_cartesian(ylim=c(1,NA)) +
    annotation_logticks(sides='lr') +
    labs(title=paste(i, ': new, total &', W, '-day rolling sum, per', P, 'residents'), y='', x='') +
    theme_bw() + 
    theme(legend.position = 'none',
                panel.grid = element_blank())


p2a <- relative_plot(DT, i, 'total_Cases', 'roll_Cases', case_col)
p2b <- relative_plot(DT, i, 'total_Deaths', 'roll_Deaths', death_col)

p3a <- rate_plot(DT, i, 'roll_Cases_Rate', 'Daily change rates', case_col)
p3b <- rate_plot(DT, i, 'roll_Deaths_Rate', '', death_col)


refDT <- subDT[Country==i,]


print( p1 / (p2a / p2b) / (p3a / p3b))

Countries of personal interest

for (i in topInterest[topInterest != 'Austria']) {
    # i <- topInterest[2]
    message(i)
    
    subDT <- melt(DT[, .(Date, Country, norm_new_Cases, norm_new_Deaths, norm_tot_Cases, norm_tot_Deaths, norm_roll_Cases, norm_roll_Deaths)],
                                id.vars = c('Date', 'Country'), variable.name = 'Type', value.name = 'Normalized_count')
    subDT[grepl('Death', Type), vsplit := 'Deaths']
    subDT[!grepl('Death', Type), vsplit := 'Cases']
    subDT[, hsplit := sub('_Cases|_Deaths', '', sub('norm_', '', Type), perl=TRUE)]
    
    p1 <- ggplot(subDT, aes(x=Date, y=Normalized_count, group=Country, colour=vsplit)) +
        facet_grid(hsplit ~ vsplit, scales = 'free_y') +
        geom_line(colour='#000000', alpha=0.1, size=0.25) +
        geom_line(data=subDT[Country==i,], size=0.75) +
        scale_y_log10() +
        scale_colour_manual(values=c(Deaths=death_col, Cases=case_col)) +
        coord_cartesian(ylim=c(1,NA)) +
        annotation_logticks(sides='lr') +
        labs(title=paste(i, ': new, total &', W, '-day rolling sum, per', P, 'residents'), y='', x='') +
        theme_bw() + 
        theme(legend.position = 'none',
                    panel.grid = element_blank())
    

    p2a <- relative_plot(DT, i, 'total_Cases', 'roll_Cases', case_col)
    p2b <- relative_plot(DT, i, 'total_Deaths', 'roll_Deaths', death_col)

    p3a <- rate_plot(DT, i, 'roll_Cases_Rate', 'Daily change rates', case_col)
    p3b <- rate_plot(DT, i, 'roll_Deaths_Rate', '', death_col)

    
    subDT <- merge(refDT, subDT[Country==i,], by=c('Date', 'Type'))
    subDT[, relative := Normalized_count.y / Normalized_count.x]
    
    p4 <- ggplot(subDT[hsplit.y=='roll',], aes(x=Date, y=relative, colour=vsplit.y)) +
        facet_grid(hsplit.y ~ vsplit.y, scales = 'free_y') +
        geom_hline(yintercept = 1, size=0.15) +
        geom_line(size=0.75) +
        scale_y_continuous(trans='log2', breaks=c(1/32,1/16,1/8,1/4,1/2,1,2,4,8,16,32)) +
        scale_colour_manual(values=c(Deaths=death_col, Cases=case_col)) +
        coord_cartesian(ylim=c(1/33, 33)) +
        # annotation_logticks(sides='lr') +
        labs(title=paste(i, 'relative to Austria'), y='', x='') +
        theme_bw() + 
        theme(legend.position = 'none',
                    axis.text.y.left = element_text())
    
    print( p1 / (p2a / p2b) / p4)
}

## Italy

## Greece

## Luxembourg

Neighbours

for (i in c('Germany', 'Switzerland', 'Slovakia', 'Slovenia', 'Czechia', 'Hungary') ) {
    message(i)
    
    subDT <- melt(DT[, .(Date, Country, norm_new_Cases, norm_new_Deaths, norm_tot_Cases, norm_tot_Deaths, norm_roll_Cases, norm_roll_Deaths)],
                                id.vars = c('Date', 'Country'), variable.name = 'Type', value.name = 'Normalized_count')
    subDT[grepl('Death', Type), vsplit := 'Deaths']
    subDT[!grepl('Death', Type), vsplit := 'Cases']
    subDT[, hsplit := sub('_Cases|_Deaths', '', sub('norm_', '', Type), perl=TRUE)]
        
    p1 <- ggplot(subDT, aes(x=Date, y=Normalized_count, group=Country, colour=vsplit)) +
        facet_grid(hsplit ~ vsplit, scales = 'free_y') +
        geom_line(colour='#000000', alpha=0.1, size=0.25) +
        geom_line(data=subDT[Country==i,], size=0.75) +
        scale_y_log10() +
        scale_colour_manual(values=c(Deaths=death_col, Cases=case_col)) +
        coord_cartesian(ylim=c(1,NA)) +
        annotation_logticks(sides='lr') +
        labs(title=paste(i, ': new, total &', W, '-day rolling sum, per', P, 'residents'), y='', x='') +
        theme_bw() + 
        theme(legend.position = 'none',
                    panel.grid = element_blank())
    
    p2a <- relative_plot(DT, i, 'total_Cases', 'roll_Cases', case_col)
    p2b <- relative_plot(DT, i, 'total_Deaths', 'roll_Deaths', death_col)

    
    p3a <- rate_plot(DT, i, 'roll_Cases_Rate', paste('Daily change rate of ', W, '-day rolling sum of cases'), case_col)
    p3b <- rate_plot(DT, i, 'roll_Deaths_Rate', paste('Daily change rate of ', W, '-day rolling sum of deaths'), death_col)
            
    print( p1 / (p2a / p2b) / (p3a / p3b))
}

## Germany

## Switzerland

## Slovakia

## Slovenia

## Czechia

## Hungary

Others

for (i in c('United_Kingdom', 'United_States_of_America', 'Sweden', 'South_Korea', 'Turkey', 'Egypt', 'Croatia', 'Malta', 'Spain') ) {
    message(i)
    
    subDT <- melt(DT[, .(Date, Country, norm_new_Cases, norm_new_Deaths, norm_tot_Cases, norm_tot_Deaths, norm_roll_Cases, norm_roll_Deaths)],
                                id.vars = c('Date', 'Country'), variable.name = 'Type', value.name = 'Normalized_count')
    subDT[grepl('Death', Type), vsplit := 'Deaths']
    subDT[!grepl('Death', Type), vsplit := 'Cases']
    subDT[, hsplit := sub('_Cases|_Deaths', '', sub('norm_', '', Type), perl=TRUE)]
        
    p1 <- ggplot(subDT, aes(x=Date, y=Normalized_count, group=Country, colour=vsplit)) +
        facet_grid(hsplit ~ vsplit, scales = 'free_y') +
        geom_line(colour='#000000', alpha=0.1, size=0.25) +
        geom_line(data=subDT[Country==i,], size=0.75) +
        scale_y_log10() +
        scale_colour_manual(values=c(Deaths=death_col, Cases=case_col)) +
        coord_cartesian(ylim=c(1,NA)) +
        annotation_logticks(sides='lr') +
        labs(title=paste(i, ': new, total &', W, '-day rolling sum, per', P, 'residents'), y='', x='') +
        theme_bw() + 
        theme(legend.position = 'none',
                    panel.grid = element_blank())
    
    p2a <- relative_plot(DT, i, 'total_Cases', 'roll_Cases', case_col)
    p2b <- relative_plot(DT, i, 'total_Deaths', 'roll_Deaths', death_col)

    
    p3a <- rate_plot(DT, i, 'roll_Cases_Rate', paste('Daily change rate of ', W, '-day rolling sum of cases'), case_col)
    p3b <- rate_plot(DT, i, 'roll_Deaths_Rate', paste('Daily change rate of ', W, '-day rolling sum of deaths'), death_col)
            
    print( p1 / (p2a / p2b) / (p3a / p3b))
}

## United_Kingdom

## United_States_of_America

## Sweden

## South_Korea

## Turkey

## Egypt

## Croatia

## Malta

## Spain